Point cloud based phenotypic parameter extraction of Chinese white cabbage
A phenotype parameter extraction system was proposed to replace the inaccurate manual measurement.Leaf area and average leaf inclination angle were utilized by collecting the original RGB image and the deep-seated image of Chinese white cabbage.The data was then synthesized and passed into a color point cloud for preprocessing.Segmentation based on hypervoxel clustering was applied to separate each leaf from the crop point cloud,and greedy projection triangulation was adopted to obtain the best surface reconstruction.As a result,color rendering for the mesh model was realized,for which the optimization was completed in the VTK library to obtain a more realistic model.The extraction of two parameters was realized in the grid model,which was compared with the manual solution.For the 4th group of leaves larger than 4 cm,the absolute error of the average inclination angle was less than 5.5°,which verified the feasibility of automated non-destructive monitoring for Chinese white cabbage.
万方数据
维普
